In image processing and computer vision contexts, color based skin detection techniques may be used in a wide range of applications such as digital make up, video based beautification, face tracking, 3-dimensional face modeling, hand gesture detection and tracking, people retrieval from databases, and the like. Furthermore, such applications are becoming increasingly popular particularly on camera embedded mobile devices such as smart phones, tablets, and the like. Therefore, robust and efficient skin detection technologies may be of increasing importance.
Skin detection techniques may seek to categorize each pixel in an image into a skin or non-skin class. In such contexts, the choice of color space for representing image pixels, the technique used for modeling and classifying skin, and the technique used for adapting to dynamic variations in video sequences may be three important factors. For example, many color spaces such as the red, green, blue (RGB) color space and linear and non-linear transformations from RGB such as the hue, saturation, value (HSV) representation of the RGB color space, the luma, blue difference, red difference (YCbCr) encoding of the RGB color space, the CIE-Lab color space, or the like may be used in skin detection. Furthermore, a variety of offline classifiers may be used in skin detection. Such classifiers may be trained via laborious and costly offline training such as allocating and annotating billions of training pixels. Furthermore, such classifiers may only be applicable and/or adaptable to a limited range of application scenarios and their performance may degrade sharply when used in unconstrained environments. To attain improved results in such environments, some models propose to update parameters of the offline training classifiers over time. However, such techniques are prone to problems with the introduction of unexpected errors from using false positives and other problems.
It may be advantageous to perform skin detection with high accuracy, easier implementation, and with less computational and memory resource requirements. It is with respect to these and other considerations that the present improvements have been needed. Such improvements may become critical as the desire to perform skin detection becomes more widespread.